Projects within the BCCN:

The major goal of this project was to develop statistical and machine learning tools for extracting predictive signatures of the underlying network dynamics from electrophysiological or neuroimaging measurements that could potentially be harvested in later clinical applications. Toward this goal, we have first combined approaches from nonlinear dynamics (delay embedding theorems) and statistical learning theory (polynomial basis expansions, kernel techniques) to reconstruct from noisy in-vivo multiple single unit (MSU) recordings attractor dynamics during behavioral task performance (Balaguer-Ballester et al. 2011). The particular challenges here were that even with modern recording techniques only a miniscule proportion of all neurons within a given brain area are usually accessed, implying that neural trajectories may not properly unfold and the flow may not be discernible within the accessed space, and that attractors within this space may have highly complicated geometries. Our combination of techniques did not only reveal task-phase-specific attracting dynamics (Balaguer-Ballester et al. 2011), but clearly distinguished between different pharmacological conditions/ phenotypes relevant to schizophrenia, with low doses of amphetamine enhancing and high doses diminishing semi-attracting states (Lapish et al. 2015), in line with earlier, model-based theoretical predictions (Durstewitz & Seamans 2008). We attempted to transfer part of these ideas to fMRI measurements from human subjects which were addressed by combinations of multivariate time series analysis tools and novel bootstrap- and classifier-based statistical tests (Demanuele et al. 2015a; Demanuele et al. 2015b), and could, by virtue of these approaches, reveal area-specific processing strategies of the same task input (assessed on translational, schizophrenia-relevant multiple-item working memory [see C8] and probabilistic sampling/ decision making tasks). In general, project D2 has also supported in-vivo MSU time series in other projects within our BCCN (C7, C8; Bähner et al. 2015; Richter et al. 2013) or with external collaboration partners (Hyman et al. 2012), the latter also related to information processing in prefrontal and hippocampal brain circuits as two of the structures most intimately involved in cognitive dysfunction in schizophrenia (Meyer-Lindenberg et al. 2005). Partly through support by this project, we had also started to work on another, in our minds highly innovative “high-throughout” approach for extracting spatio-temporal patterns on different time scales from physiological measurements (Russo & Durstewitz 2017), based on fast parametric statistical assessment, an efficient algorithm for dealing with combinatorial explosion problems in this context, and novel methods to account for non-stationarity in the data.